Health Care – A Personalized Guidance for Non-Communicable Diseases

Dakshima, D.D.T.D; Mindula, K Seliya; Rathnayake, R.M.S.J; Kasthuriarachchi, Sanvitha; Chathuranga, A.K Buddhi; Lunugalage, Dilani

doi:10.1109/icac57685.2022.10025109

Cited by 2 publications

(1 citation statement)

References 13 publications

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“…Knowing how many calories are burned while exercising might help people make more informed diet and exercise choices. Calories burned can be estimated by a number of methods such as metabolic equations [5], heart rate [5], wearable fitness trackers [6], and advanced sensors [7] by taking into account characteristics such as age, gender, weight, height, activity type, heart rate, exercise duration and intensity. Prediction of calories burned can be made using machine learning predictive models.…”

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confidence: 99%

Comparison of Machine Learning Methods for Calories Burn Prediction

Tan,

Che Embi,

Hashim

2024

JIWE

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This paper focuses on the prediction of calories burned during exercise using machine learning techniques. Due to a growing number of obesity and overweight people, a healthy lifestyle must be adopted and maintained. This study explores and compares several machine learning regression models namely LightGBM, XGBoost, Random Forest, Ridge, Linear, Lasso, and Logistic to assess their calories burned prediction performance that can be used in systems such as fitness recommender systems supporting a healthy lifestyle. Our findings show that the LightGBM for predicting calorie burn has a good accuracy of 1.27 mean absolute error, giving users reliable recommendations. The proposed system has a good potential in assisting users in reaching their fitness objectives by offering precise and tailored advice.

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confidence: 99%